{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "440d361e-f41e-494a-98a3-aa87cf0087af",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-12-25T16:53:12.070274Z",
     "end_time": "2023-12-25T16:53:13.239212Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "       销售变化\n一月销售额  1000\n上海       30\n北京      -70\n浙江      -80\n江苏     -110\n广东     -120\n湖北     -150\n二月销售额   500",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>销售变化</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>一月销售额</th>\n      <td>1000</td>\n    </tr>\n    <tr>\n      <th>上海</th>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>北京</th>\n      <td>-70</td>\n    </tr>\n    <tr>\n      <th>浙江</th>\n      <td>-80</td>\n    </tr>\n    <tr>\n      <th>江苏</th>\n      <td>-110</td>\n    </tr>\n    <tr>\n      <th>广东</th>\n      <td>-120</td>\n    </tr>\n    <tr>\n      <th>湖北</th>\n      <td>-150</td>\n    </tr>\n    <tr>\n      <th>二月销售额</th>\n      <td>500</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 正常显示中文标签\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "# 正常显示负号\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "# 自动适应布局，设置字体大小\n",
    "plt.rcParams.update({'figure.autolayout': True, 'font.size': 16})\n",
    "# 禁用科学计数法\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "\n",
    "# 读取数据\n",
    "sale = pd.read_excel('不同地区的销售额变化.xlsx', index_col=0)\n",
    "\n",
    "# 处理数据\n",
    "# 计算销售额的变化\n",
    "sale['销售变化'] = sale.iloc[:, 1] - sale.iloc[:, 0]\n",
    "# 把销售汇总作为第一行\n",
    "change = pd.concat([pd.DataFrame(sale.sum()).T, sale])\n",
    "# 修改第一行的索引名称：上个月\n",
    "change.rename(index={0: sale.columns[0]}, inplace=True)\n",
    "# 设置瀑布图的第一个数值\n",
    "change.iloc[0, 2] = change.iloc[0, 0]\n",
    "# 排除没有变化的项目\n",
    "change = change[abs(change['销售变化']) > 0]\n",
    "# 降序排列\n",
    "change = change.sort_values('销售变化', ascending=False)\n",
    "# 不要包含汇总，后面会自动计算\n",
    "index = change.index\n",
    "data = change['销售变化']\n",
    "trans = pd.DataFrame(data=data, index=index)\n",
    "# 为瀑布图创建空白序列，用于把柱子撑起来\n",
    "blank = trans['销售变化'].cumsum().shift(1).fillna(0)\n",
    "# 计算瀑布图的最后一个数值：当月收入\n",
    "thismonth = sale.columns[1]\n",
    "total = trans.sum()['销售变化']\n",
    "# 设置瀑布图中每个项目的变化\n",
    "step = blank.reset_index(drop=True).repeat(3).shift(-1)\n",
    "step[1::3] = np.nan\n",
    "# 最后一根柱子是从0开始\n",
    "blank.loc[thismonth] = 0\n",
    "trans.loc[thismonth] = total\n",
    "trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_22272\\4006728260.py:10: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n",
      "  plot_offset = int(max / 10)\n",
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_22272\\4006728260.py:21: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  y = y_height[loop] + x\n",
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_22272\\4006728260.py:19: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  y = y_height[loop]\n",
      "C:\\Python311\\Lib\\site-packages\\matplotlib\\text.py:1463: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  y = float(self.convert_yunits(y))\n",
      "C:\\Python311\\Lib\\site-packages\\matplotlib\\text.py:895: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "C:\\Python311\\Lib\\site-packages\\matplotlib\\text.py:754: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  posy = float(self.convert_yunits(self._y))\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1600x800 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 开始画图\n",
    "# 使用 Pandas 中的画图函数\n",
    "waterfall = trans.plot(kind='bar', stacked=True, bottom=blank, legend=None, figsize=(16, 8), fontsize=20, width=0.8)\n",
    "# 设置 x 轴标签\n",
    "waterfall.set_xlabel('')\n",
    "# 计算标签位置的偏移量\n",
    "max = trans.max()\n",
    "neg_offset = max / 25\n",
    "pos_offset = max / 50\n",
    "plot_offset = int(max / 10)\n",
    "# 获取标签的高度位置\n",
    "y_height = trans['销售变化'].cumsum().shift(1).fillna(0)\n",
    "# 循环设置标签\n",
    "loop = 0\n",
    "for index, row in trans.iterrows():\n",
    "    x = row['销售变化']\n",
    "    # 最后一个标签的位置不要双倍\n",
    "    if x == total:\n",
    "        y = y_height[loop]\n",
    "    else:\n",
    "        y = y_height[loop] + x\n",
    "    # 决定向上还是向下偏移\n",
    "    if x > 0:\n",
    "        y += pos_offset\n",
    "    else:\n",
    "        y -= neg_offset\n",
    "    # 添加数字标签，负数用红色\n",
    "    waterfall.annotate('{:.1f}'.format(x), (loop, y), ha='center', fontsize=20, color=('k' if x > 0 else 'r'))\n",
    "    loop += 1\n",
    "\n",
    "# 设置 y 轴的偏移量\n",
    "waterfall.set_ylim(0, blank.max() + int(plot_offset))\n",
    "# 设置 x 轴标签的角度\n",
    "waterfall.set_xticklabels(trans.index, rotation=0)\n",
    "# 设置标题并显示图片\n",
    "plt.title('\\n各地区销售额变化瀑布图\\n', fontsize=36, loc='center', color='k')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-12-25T16:53:13.239212Z",
     "end_time": "2023-12-25T16:53:13.538854Z"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
